A Comparison of X-Ray Image Segmentation Techniques

Stolojescu-Crisan, Cristina; Holban, Ştefan

doi:10.4316/aece.2013.03014

Cited by 59 publications

(34 citation statements)

References 26 publications

(30 reference statements)

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“…Cristina et al [7] Contributions: Segmentation based on deformable or atlas is considered more appropriate for complex segmentation of medical images.…”

Section: Authors Descriptionsmentioning

confidence: 99%

Recognition of X-rays bones: Challenges in the Past, Present and Future

Pandey¹,

Soam²,

Tripathi³

2019

IJITEE

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Image processing tool is a key role in medical uses, by extracting anatomical features the contouring of the regions from medical images. The discovery of bone sketch from X-ray images has recently introduced a new dimension in the literature as it proves to be an important step of radiological imaging analysis. X-ray images are segmented to study bone skeleton where the image is split into several parts, to examine the fracture of bones, and to plan for treatment before surgery. This report's, purpose is to survey digital image splitting methods. In image segmentation, research analysis is important for categories and it is important to provide an overview of assistive segmentation techniques with highlighting advantage and disadvantage.

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“…Cristina et al [7] Contributions: Segmentation based on deformable or atlas is considered more appropriate for complex segmentation of medical images.…”

Section: Authors Descriptionsmentioning

confidence: 99%

Recognition of X-rays bones: Challenges in the Past, Present and Future

Pandey¹,

Soam²,

Tripathi³

2019

IJITEE

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“…Here utilized neural system to distinguish break in long bones and should be made reference to that their recreation result demonstrate that how NN perform location of the crack in leg radiograph. The best correlation of x-beam picture division strategies has been presented in [10]. The strategies are thresholding, areabased strategies, edge-based division techniques, bunching or group investigation, characterization based division procedures, and level set strategies, Active form models, Active shape models, Wavelet-based systems and knowledgebased methods.…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures

Mohammad¹,

Rubayat²,

Al³

2018

CAE

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Distinguishing human hand fingers bone break is an extremely basic issue in restorative. The framework proposed another approach to recognize these sorts of crack by removing highlights. For doing the general procedure among MRI (Magnetic Resonance Imaging), CT (Computed Tomography) and X-beam pictures, the proposed framework utilized X-beam pictures. At first, the framework takes information from different orthopedic foundations. Subsequent to getting the information the picture preprocessing steps have been done: right off the bat pictures have been changed over into dark, at that point sifted lastly into parallel pictures. From twofold pictures the GLCM (Gray Level Co-event Matrix), minute highlights, entropy, real pivot length, minor hub length, erraticism, introduction, arched region, zone, filled zone, equiv breadth, robustness, degree, border, mean, standard deviation, relationship coefficient, middle, fluctuation, proportion, pixel, and Euclidian separation has been removed. The element esteems are prepared by the Artificial Neural Network (ANN) where the framework used to encourage forward back proliferation systems. At that point, the yield gives two qualities where it is in typical or crack. The figure of prepared Neural Network gives the execution, preparing state, relapse of the trial which is high. The tables decided the gatherings which are changed into size and shapes and furthermore gives that the pictures are in typical or crack with precision 92.24% which is superior to other. The proposed framework can effectively distinguish the pictures of crack and typical yet can't recognize its composes. Later on, the framework will attempt to test about it.

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“…Generally, 3 main steps are involved in classification system; (1) segmentation, (2) feature extraction, and (3) classification. Literature review of related published work shows that many researchers have worked on segmentation techniques and proposed several automatic and semi‐automatic segmentation techniques . Automatic techniques are computationally complex and time taking, while in semi‐automatic techniques the neoplasm region is marked by user, and require less processing time.…”

Section: Related Workmentioning

confidence: 99%

A new rectangular window based image cropping method for generalization of brain neoplasm classification systems

Zia¹,

Akhtar

Aziz³

2017

Int J Imaging Syst Tech

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Classification of brain neoplasm images is one of the most challenging research areas in the field of medical image processing. The main objective of this study is to design a brain neoplasm classification system that can be trained using multiple various sized MR images of different institutions. The proposed method is a generalized classification system; it can be used in a single institute or in a number of institutions at the same time, without any restriction on image size. The generalization and unbiased capability of the proposed method can bring researchers on a single platform to work on some standard forms of computer aided diagnosis system with more efficient diagnostic capabilities. In this study, a suitable size of moveable rectangular window is used between segmentation and feature extraction stages. A semiautomatic, localized region based active contour method is used for segmentation of brain neoplasm region. Discrete wavelet transform for feature extraction, principal component analysis for feature selection and support vector machine is used as classifier. For the first time MR images of 2 sizes and from different institutions are used in training and testing of brain neoplasm classifier. Three glioma grades were classified using 92 MR images. The proposed method achieved the highest accuracy of 88.26%, the highest sensitivity of 92.23% and the maximum specificity of 93.93%. In addition, the proposed method is computationally less complex, requires shorter processing time and is more efficient in terms of storage capacity.

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A Comparison of X-Ray Image Segmentation Techniques

Cited by 59 publications

References 26 publications

Recognition of X-rays bones: Challenges in the Past, Present and Future

Recognition of X-rays bones: Challenges in the Past, Present and Future

An Approach for Locating Human Hand Fingers Bone Break from X-beam Pictures

A new rectangular window based image cropping method for generalization of brain neoplasm classification systems

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